sklearn 中的 Pipeline 机制 和FeatureUnion

1、pipeline的用法

pipeline能够用于把多个estimators级联成一个estimator，这么 作的缘由是考虑了数据处理过程当中一系列先后相继的固定流程，好比feature selection->normalization->classification

pipeline提供了两种服务：

• Convenience:只须要调用一次fit和predict就能够在数据集上训练一组estimators
• Joint parameter selection能够把grid search 用在pipeline中全部的estimators参数的参数组合上面

注意：Pipleline中最后一个以外的全部estimators都必须是变换器（transformers），最后一个estimator能够是任意类型（transformer，classifier，regresser）

若是最后一个estimator是个分类器，则整个pipeline就能够做为分类器使用，若是最后一个estimator是个聚类器，则整个pipeline就能够做为聚类器使用。

from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression

from sklearn.pipeline import Pipeline

estimator=[('pca', PCA()),
           ('clf', LogisticRegression())
           ]
pipe=Pipeline(estimator)
print(pipe)
#Pipeline(steps=[('pca', PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,svd_solver='auto', tol=0.0, whiten=False)), ('clf', LogisticRegression(C=1.0, class_weight=None, dual=False,fit_intercept=True,intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,penalty='l2', random_state=None, solver='liblinear', tol=0.0001,verbose=0, warm_start=False))])
print(pipe.steps[0])
#('pca', PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,  svd_solver='auto', tol=0.0, whiten=False))
print(pipe.named_steps['pca'])
#PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,  svd_solver='auto', tol=0.0, whiten=False)

在pipeline中estimator的参数经过使用<estimator>__<parameter>语法来获取

#修改参数并打印输出
print(pipe.set_params(clf__C=10))
#Pipeline(steps=[('pca', PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,svd_solver='auto', tol=0.0, whiten=False)), ('clf', LogisticRegression(C=10, class_weight=None, dual=False,fit_intercept=True,intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,penalty='l2', random_state=None, solver='liblinear', tol=0.0001,verbose=0, warm_start=False))])

既然有参数的存在，就可使用网格搜索方法来调节参数

from sklearn.model_selection import GridSearchCV
params=dict(pca__n_components=[2,5,10],clf__C=[0,1,10,100])
grid_research=GridSearchCV(pipe,param_grid=params)

单个阶段（step）能够用参数替换，并且非最后阶段还能够将其设置为None来忽略：

from sklearn.linear_model import LogisticRegression
params=dict(pca=[None,PCA(5),PCA(10)],clf=[SVC(),LogisticRegression()],
            clf_C=[0.1,10,100])
grid_research=GridSearchCV(pipe,param_grid=params)

函数make_pipeline是一个构造pipeline的简短工具，他接受可变数量的estimators并返回一个pipeline，每一个estimator的名称自动填充。

from sklearn.pipeline import make_pipeline
from sklearn.naive_bayes import MultinomialNB
from sklearn.preprocessing import Binarizer
print(make_pipeline(Binarizer(),MultinomialNB()))

#Pipeline(steps=[('binarizer', Binarizer(copy=True, threshold=0.0)), ('multinomialnb', MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True))])

FeatureUnion：composite（组合）feature spaces

FeatureUnion把若干个transformer objects组合成一个新的transformer，这个新的transformer组合了他们的输出，一个FeatureUnion对象接受一个transformer对象列表

2、FeatureUnion 的用法

from sklearn.pipeline import FeatureUnion
from sklearn.decomposition import PCA
from sklearn.decomposition import KernelPCA
estimators=[('linear_pca',PCA()),('kernel_pca',KernelPCA())]
combined=FeatureUnion(estimators)
print(combined)

#FeatureUnion(n_jobs=1, transformer_list=[('linear_pca', PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,  svd_solver='auto', tol=0.0, whiten=False)), ('kernel_pca', KernelPCA(alpha=1.0, coef0=1, copy_X=True, degree=3, eigen_solver='auto',     fit_inverse_transform=False, gamma=None, kernel='linear',     kernel_params=None, max_iter=None, n_components=None, n_jobs=1,  random_state=None, remove_zero_eig=False, tol=0))],transformer_weights=None)

与pipeline相似，feature union也有一种比较简单的构造方法：make_union,不须要显示的给每一个estimator指定名称。

 Featu热Union设置参数

#修改参数
print(combined.set_params(kernel_pca=None))

#FeatureUnion(n_jobs=1,transformer_list=[('linear_pca', PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,svd_solver='auto', tol=0.0, whiten=False)), ('kernel_pca', None)],transformer_weights=None)

 另一篇讲pipleline不错的文章：http://blog.csdn.net/lanchunhui/article/details/50521648